
Approximate Core Allocations for Multiple Partners Matching Games
The multiple partners matching game is a cooperative profitsharing game...
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Attaining Fairness in Communication for Omniscience
This paper studies how to attain fairness in communication for omniscien...
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Two Birds With One Stone: Fairness and Welfare via Transfers
We study the question of dividing a collection of indivisible goods amon...
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Coreness of Cooperative Games with Truncated Submodular Profit Functions
Coreness represents solution concepts related to core in cooperative gam...
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Pseudo Polynomial Size LP Formulation for Calculating the Least Core Value of Weighted Voting Games
In this paper, we propose a pseudo polynomial size LP formulation for fi...
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Computing the egalitarian allocation with network flows
In a combinatorial exchange setting, players place sell (resp. buy) bids...
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Fair cost allocation for ridesharing services  modeling, mathematical programming and an algorithm to find the nucleolus
This paper addresses one of the most challenging issues in designing an ...
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The Empirical Core of the Multicommodity Flow Game Without Side Payments
Policy makers focus on stable strategies as the ones adopted by rational players. If there are many such solutions an important question is how to select amongst them. We study this question for the Multicommodity Flow Coalition Game, used to model cooperation between autonomous systems (AS) in the Internet. In short, strategies are flows in a capacitated network. The payoff to a node is the total flow which it terminates. MarkakisSaberi show this game is balanced and hence has a nonempty core by Scarf's Theorem. In the transferable utility (TU) version this gives a polynomialtime algorithm to find core elements, but for ASs side payments are not natural. Finding core elements in NTU games tends to be computationally more difficult. For this game, the only previous result gives a procedure to find a core element when the supply graph is a path. We extend their work with an algorithm, Incorporate, to which produces many different core elements. We use our algorithm to evaluate specific instances by generating many core vectors. We call these the Empirical Core for a game. We find that sampled core vectors are more consistent with respect to social welfare (SW) than for fairness (minimum payout). For SW they tend to do as well as the optimal linear program value LP_sw. In contrast, there is a larger range in fairness for the empirical core; the fairness values tend to be worse than the optimal fairness LP value LP_fair. We study this discrepancy in the setting of general graphs with singlesink demands. We give an algorithm which produces core vectors that simultaneously maximize SW and fairness. This leads to the following bicriteria result for general games. Given any coreproducing algorithm and any λ∈ (0,1), one can produce an approximate core vector with fairness (resp. social welfare) at least λ LP_fair (resp (1λ) LP_sw).
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